Single tool 'audit_bio' has a clear verb-noun name and reasonable description (156 chars), but critical gaps undermine quality. Input schema is visible with two parameters (bio, platform), both typed as strings with descriptions. However, the output schema is NOT documented in the tool definition, the code shows a return object with 'verdict', 'char_count', 'issues', and 'platform_rules' fields, but this structure is not declared in the tool's registered schema. Parameter 'platform' lacks an enum constraint despite accepting only 5 known values (tiktok, instagram, linkedin, x, youtube), forcing the LLM to guess valid options. No error handling guidance is present, the tool silently returns issues without explaining recovery steps. The tool is read-only (low risk), but lacks the structured output documentation and input validation patterns expected of production tools.

• Add explicit output schema to tool definition: {verdict: 'PASS'|'NEEDS WORK', char_count: number, issues: [{rule: string, detail: string, severity?: string}], platform_rules?: object}
• Convert platform parameter to enum: ['tiktok', 'instagram', 'linkedin', 'x', 'youtube'] with description 'Optional platform for platform-specific character limit checks'
• Expand bio parameter description: 'The personal brand bio text to audit (typically 80-220 chars depending on platform). Should contain outcome language, receipts (numbers/proof), and avoid hype words.'
• Add error handling guidance to tool description: 'Returns verdict (PASS/NEEDS WORK) and a list of issues. If issues exist, use the detail field to guide rewrites. No issues = bio is platform-ready.'
• Document the issues array structure in description: each issue has rule (char_limit, hype_title, no_receipt, title_first, no_outcome), detail (actionable fix), and optional severity (consider = non-blocking)